Background réaliste CsI(Tl) + hybridation mesuré/synthétique + dashboard continuum
- Remplace le continuum exponentiel par un modèle réaliste CsI(Tl) dans l'entraînement (bosse asymétrique ~110 keV + queue Compton) - Ajoute l'injection de background mesuré (70% mesuré / 30% synthétique) via --measured_background et MEASURED_BACKGROUND_PATH - Ajoute l'endpoint /api/background/continuum et le toggle "Continuum CsI" sur le dashboard background - Exclut le canal 1023 (overflow bin) de l'affichage web (NUM_CHANNELS=1023) - Corrige le lissage Gaussien du background (normalisation locale aux bords) - Met à jour README.md, CLAUDE.md, TUTORIEL.md, TOTO.md, vega_ml/README.md Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -10,7 +10,7 @@ ISOTOPE_INDEX_PATH = Path(os.environ.get("ISOTOPE_INDEX_PATH", "/models/vega_iso
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ENERGY_OFFSET = float(os.environ.get("ENERGY_CALIBRATION_OFFSET", "0.33"))
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ENERGY_SLOPE = float(os.environ.get("ENERGY_CALIBRATION_SLOPE", "2.97"))
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NUM_CHANNELS = 1024
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NUM_CHANNELS = 1023 # Last channel (1023) is overflow bin, excluded from display
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def energy_axis():
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@ -1,24 +1,41 @@
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import json
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from fastapi import APIRouter, HTTPException
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from app.config import BACKGROUND_SNAPSHOT_PATH, BACKGROUND_PATH, energy_axis, NUM_CHANNELS
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from app.theoretical_bg import generate_theoretical_bg, generate_continuum_only
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import numpy as np
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router = APIRouter()
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@router.get("")
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async def get_background_info():
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"""Background metadata: elapsed time, CPS, top peaks."""
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def _load_snapshot():
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"""Load the live snapshot file, or raise 404."""
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if not BACKGROUND_SNAPSHOT_PATH.exists():
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raise HTTPException(status_code=404, detail="Background capture not available yet")
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try:
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with open(BACKGROUND_SNAPSHOT_PATH) as f:
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snapshot = json.load(f)
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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raise HTTPException(status_code=500, detail="Background snapshot file corrupt")
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# Check if full background is available
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def _load_reference():
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"""Load the 24h reference background, or return None."""
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if not BACKGROUND_PATH.exists():
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return None
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try:
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bg_data = np.load(str(BACKGROUND_PATH), allow_pickle=True).item()
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return {
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"counts": [round(float(c), 1) for c in bg_data["counts"][:NUM_CHANNELS]],
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"live_time_s": round(float(bg_data["duration"]), 1),
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}
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except Exception:
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return None
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@router.get("")
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async def get_background_info():
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"""Background metadata: elapsed time, CPS, top peaks."""
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snapshot = _load_snapshot()
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full_available = BACKGROUND_PATH.exists()
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return {
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@ -33,34 +50,46 @@ async def get_background_info():
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@router.get("/spectrum")
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async def get_background_spectrum():
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"""Full background spectrum with energy axis."""
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if not BACKGROUND_SNAPSHOT_PATH.exists():
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raise HTTPException(status_code=404, detail="Background capture not available yet")
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try:
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with open(BACKGROUND_SNAPSHOT_PATH) as f:
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snapshot = json.load(f)
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except (json.JSONDecodeError, OSError):
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raise HTTPException(status_code=500, detail="Background snapshot file corrupt")
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counts = snapshot.get("spectrum", [0] * NUM_CHANNELS)
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# If full background file exists, use it for better data
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if BACKGROUND_PATH.exists():
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try:
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bg_data = np.load(str(BACKGROUND_PATH), allow_pickle=True).item()
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counts = [round(float(c), 1) for c in bg_data["counts"]]
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live_time = float(bg_data["duration"])
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except Exception:
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live_time = snapshot.get("live_time_s", 0)
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else:
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live_time = snapshot.get("live_time_s", 0)
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"""Live background spectrum (from snapshot) with energy axis."""
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snapshot = _load_snapshot()
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live_time = snapshot.get("live_time_s", 0)
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return {
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"channels": list(range(NUM_CHANNELS)),
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"energy_kev": energy_axis(),
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"counts": counts,
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"counts": snapshot.get("spectrum", [0] * 1024)[:NUM_CHANNELS],
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"live_time_s": live_time,
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"cps": snapshot.get("cps", 0),
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"top_peaks": snapshot.get("top_peaks", []),
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}
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"reference_available": BACKGROUND_PATH.exists(),
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}
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@router.get("/reference")
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async def get_background_reference():
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"""24h reference background spectrum for overlay comparison."""
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ref = _load_reference()
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if ref is None:
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raise HTTPException(status_code=404, detail="No 24h reference background available")
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return {
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"channels": list(range(NUM_CHANNELS)),
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"energy_kev": energy_axis(),
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"counts": ref["counts"],
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"live_time_s": ref["live_time_s"],
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}
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@router.get("/theoretical")
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async def get_theoretical_bg(cps: float = 6.0, live_time_s: float = 3600.0):
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"""Theoretical natural background spectrum (K-40, U-238 chain, Th-232 chain)."""
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return generate_theoretical_bg(cps=cps, live_time_s=live_time_s)
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@router.get("/continuum")
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async def get_continuum(cps: float = 6.0, live_time_s: float = 3600.0):
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"""CsI(Tl) continuum shape only (hump + Compton tail, no photopeaks, no noise).
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Matches the model used in training (generate_realistic_continuum).
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"""
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return generate_continuum_only(cps=cps, live_time_s=live_time_s)
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@ -29,7 +29,7 @@ async def get_current_spectrum():
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"isotopes_detected": state.get("isotopes_detected", []),
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"channels": list(range(NUM_CHANNELS)),
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"energy_kev": energy_axis(),
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"counts": state.get("counts", [0] * NUM_CHANNELS),
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"counts": state.get("counts", [0] * 1024)[:NUM_CHANNELS],
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}
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@ -45,7 +45,7 @@ async def get_difference_spectrum():
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except (json.JSONDecodeError, OSError):
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raise HTTPException(status_code=503, detail="Monitor state file corrupt")
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counts = np.array(state.get("counts", [0] * NUM_CHANNELS), dtype=np.float64)
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counts = np.array(state.get("counts", [0] * 1024), dtype=np.float64)[:NUM_CHANNELS]
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live_time = state.get("cumulated_live_time_s", 0)
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if live_time <= 0:
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@ -55,7 +55,7 @@ async def get_difference_spectrum():
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if BACKGROUND_PATH.exists():
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bg_data = np.load(str(BACKGROUND_PATH), allow_pickle=True).item()
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bg_counts = bg_data["counts"].astype(np.float64)
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bg_counts = bg_data["counts"].astype(np.float64)[:NUM_CHANNELS]
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bg_live_time = float(bg_data["duration"])
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bg_rate = bg_counts / bg_live_time
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net_rate = np.clip(rate - bg_rate, 0, None)
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@ -72,5 +72,5 @@ async def get_difference_spectrum():
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"channels": list(range(NUM_CHANNELS)),
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"energy_kev": energy_axis(),
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"counts": [round(float(c), 1) for c in net_counts],
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"raw_counts": state.get("counts", []),
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"raw_counts": state.get("counts", [])[:NUM_CHANNELS],
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}
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139
web/app/theoretical_bg.py
Normal file
139
web/app/theoretical_bg.py
Normal file
@ -0,0 +1,139 @@
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"""
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Theoretical natural background spectrum for CsI(Tl) detectors (Radiacode 103).
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Shape calibrated against real Radiacode 103 background measurements.
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The CsI(Tl) crystal (1 cm³, 8.4% FWHM) produces a spectrum with:
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- A dominant low-energy hump peaking around 100-120 keV
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- Exponential decay at higher energies
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- Subtle photopeaks from natural isotopes
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"""
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import numpy as np
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from app.config import ENERGY_OFFSET, ENERGY_SLOPE, NUM_CHANNELS
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# Photopeak lines: (energy_keV, relative_weight)
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# Weights tuned so peaks are visible above local continuum at typical CPS
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NATURAL_BG_LINES = [
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(295.22, 0.10), # Pb-214
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(351.93, 0.18), # Pb-214
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(609.31, 0.15), # Bi-214
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(911.20, 0.08), # Ac-228
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(968.97, 0.05), # Ac-228
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(1120.29, 0.06), # Bi-214
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(1460.83, 0.12), # K-40
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(1764.49, 0.08), # Bi-214
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(2614.51, 0.18), # Tl-208
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]
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def _gaussian(x, center, sigma, amplitude):
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return amplitude * np.exp(-0.5 * ((x - center) / sigma) ** 2)
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def generate_theoretical_bg(cps: float = 6.0, live_time_s: float = 3600.0):
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channels = np.arange(NUM_CHANNELS, dtype=np.float64)
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energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
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total_counts = cps * live_time_s
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# ── 1. Main hump: asymmetric peak at ~105 keV ──
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# Real data: rises from ~60 at 10keV to ~280 at 100-120keV, then falls
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hump_center = 110.0
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hump = np.zeros(NUM_CHANNELS, dtype=np.float64)
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low_mask = energy_axis <= hump_center
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hump[low_mask] = _gaussian(energy_axis[low_mask], hump_center, 55.0, 1.0)
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hump[~low_mask] = _gaussian(energy_axis[~low_mask], hump_center, 50.0, 1.0)
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# ── 2. Compton continuum tail ──
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# Real data: ~136@200, ~80@250, ~44@295, ~14@400, ~5@600
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tail = 0.45 * np.exp(-energy_axis / 240) + 0.04 * np.exp(-energy_axis / 700)
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# ── 3. Low-energy noise floor ──
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noise_floor = 0.008
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# ── 4. Combine continuum ──
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continuum = hump + tail + noise_floor
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# ── 5. Photopeaks ──
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# CsI(Tl) 8.4% FWHM at 662 keV, scaling as sqrt(E)
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# sigma(E) = FWHM(E) / 2.355 = 0.084 * sqrt(E * 662) / 662 / 2.355
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# Simplified: sigma = 23.6 * sqrt(E/662) keV
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def sigma_keV(E):
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return max(12.0, 23.6 * np.sqrt(max(E, 1.0) / 662.0))
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peak_frac = 0.08 # 8% of total counts in resolved photopeaks
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total_weight = sum(w for _, w in NATURAL_BG_LINES)
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peaks = np.zeros(NUM_CHANNELS, dtype=np.float64)
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for line_energy, weight in NATURAL_BG_LINES:
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sig = sigma_keV(line_energy)
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peak_counts = total_counts * peak_frac * (weight / total_weight)
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amplitude = peak_counts / (sig * np.sqrt(2 * np.pi))
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peaks += _gaussian(energy_axis, line_energy, sig, amplitude)
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# ── 6. Combine and normalize ──
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raw = continuum + peaks / total_counts # peaks normalized later
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raw *= total_counts / raw.sum()
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# ── 7. Poisson-like noise ──
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rng = np.random.default_rng(42)
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noise = rng.normal(0, 1, NUM_CHANNELS) * np.sqrt(np.maximum(raw, 1.0)) * 0.25
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raw += noise
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# Floor at 0.9 for log scale
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spectrum = np.clip(raw, 0.9, None)
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key_lines = [
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(295.22, "Pb-214"), (351.93, "Pb-214"),
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(609.31, "Bi-214"), (911.20, "Ac-228"),
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(1120.29, "Bi-214"), (1460.83, "K-40"),
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(1764.49, "Bi-214"), (2614.51, "Tl-208"),
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]
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return {
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"energy_kev": [round(float(E), 2) for E in energy_axis],
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"counts": [round(float(c), 1) for c in spectrum],
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"cps": round(cps, 2),
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"live_time_s": round(live_time_s, 1),
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"lines": [
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{"energy_keV": E, "name": name} for E, name in key_lines
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],
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}
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def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
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"""Generate only the CsI(Tl) continuum shape (no photopeaks, no noise).
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This matches the model used in training (generate_realistic_continuum in
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spectrum_physics.py) for direct comparison with measured backgrounds.
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"""
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channels = np.arange(NUM_CHANNELS, dtype=np.float64)
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energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
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total_counts = cps * live_time_s
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# Asymmetric hump at ~110 keV
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hump_center = 110.0
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hump = np.where(
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energy_axis <= hump_center,
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np.exp(-0.5 * ((energy_axis - hump_center) / 55.0) ** 2),
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np.exp(-0.5 * ((energy_axis - hump_center) / 50.0) ** 2),
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)
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# Compton continuum tail
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tail = 0.45 * np.exp(-energy_axis / 240.0) + 0.04 * np.exp(-energy_axis / 700.0)
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# Noise floor
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noise_floor = 0.008
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continuum = hump + tail + noise_floor
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# Normalize to target total counts
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if continuum.sum() > 0 and total_counts > 0:
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continuum *= total_counts / continuum.sum()
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return {
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"energy_kev": [round(float(E), 2) for E in energy_axis],
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"counts": [round(float(c), 1) for c in continuum],
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"cps": round(cps, 2),
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"live_time_s": round(live_time_s, 1),
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}
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